用触觉皮肤识别用户导航意图，让机器人更懂人的动作
Tactile-Based Human Intent Recognition for Robot Assistive Navigation
- 用圆柱形触觉传感器捕捉用户抓握动作
- 算法在真实数据上准确率达90.8%，优于四种基线模型
- 用户更偏好触觉控制，比摇杆和语音更自然
机器人辅助导航（RAN）对提升行动不便人群的独立性至关重要。现有系统多依赖无法模拟人与照护者间直观物理沟通的交互界面，限制了效果。本文提出Tac-Nav系统，利用安装在Stretch 3移动操作臂上的圆柱形触觉皮肤，实现更自然高效的导航意图识别。为鲁棒分类触觉数据，开发了考虑传感器圆柱几何结构的径向核支持向量机（CK-SVM），有效应对用户抓握时的自然旋转偏移。大量实验表明，CK-SVM在仿真数据集上准确率达97.1%，真实场景下达90.8%，优于四种基线模型。此外，初步用户研究证实，用户更偏好触觉界面，相比传统摇杆和语音控制更具优势。
原文摘要 · Abstract (English)
Robot assistive navigation (RAN) is critical for enhancing the mobility and independence of the growing population of mobility-impaired individuals. However, existing systems often rely on interfaces that fail to replicate the intuitive and efficient physical communication observed between a person and a human caregiver, limiting their effectiveness. In this paper, we introduce Tac-Nav, a RAN system that leverages a cylindrical tactile skin mounted on a Stretch 3 mobile manipulator to provide a more natural and efficient interface for human navigational intent recognition. To robustly classify the tactile data, we developed the Cylindrical Kernel Support Vector Machine (CK-SVM), an algorithm that explicitly models the sensor's cylindrical geometry and is consequently robust to the natural rotational shifts present in a user's grasp. Comprehensive experiments were conducted to demonstrate the effectiveness of our classification algorithm and the overall system. Results show that CK-SVM achieved superior classification accuracy on both simulated (97.1%) and real-world (90.8%) datasets compared to four baseline models. Furthermore, a pilot study confirmed that users more preferred the Tac-Nav tactile interface over conventional joystick and voice-based controls.
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